基于改进YOLOv8算法的钢材表面缺陷检测  

Steel Surface Defect Detection Based on Improved YOLOv8 Algorithm

作　　者：徐薪羽 沈通 吕佳[1] XU Xinyu;SHEN Tong;LV Jia(College of Computer and Information Sciences,Chongqing Normal University,Chongqing 401331,China)

机构地区：[1]重庆师范大学计算机与信息科学学院,重庆401331

出　　处：《自动化应用》2024年第15期6-10,共5页Automation Application

基　　金：重庆市教委“成渝地区双城经济圈建设”科技创新项目(KJCX2020024);重庆市2023年研究生科研创新项目(CYS23407)。

摘　　要：针对钢材表面缺陷检测任务中目标尺寸较小且形态多变,传统检测方法效率较低,通用算法也难以准确捕捉其特征信息等问题,提出基于YOLOv8的改进模型。利用可变形卷积替换部分标准卷积,使模型能更好地学习采样位置的偏移,适应表面缺陷的几何变换,从而提高检测精度;引入基于辅助边框的Inner-IoU损失函数,其能根据不同的检测任务自我调整比例因子,以控制辅助边界框的生成,适合处理形状多变的表面缺陷,提高模型泛化性。在基准数据集NEU-DET上的实验结果表明,与具有相同参数量的基线模型相比,改进后的模型的检测精度提高了2.3%,且优于其他对比算法。此外,在GC10-DET数据集上,与基线模型相比,其检测精度提高了4.0%,表明该模型具有良好的泛化性。Aiming at the problems of small target size and variable shape in steel surface defect detection tasks,low efficiency of traditional detection methods,and difficulty in accurately capturing their feature information with general algorithms,an improved model based on YOLOv8 is proposed.Replacing some standard convolutions with deformable convolutions enables the model to better learn the offset of sampling positions,adapt to geometric transformations of surface defects,and thus improve detection accuracy.Introducing an Inner IoU loss function based on auxiliary bounding boxes,which can self adjust the scaling factor according to different detection tasks to control the generation of auxiliary bounding boxes,is suitable for handling surface defects with variable shapes,and improves model generalization.The experimental results on the benchmark dataset NEU-DET show that the improved model improves detection accuracy by 2.3%compared to the baseline model with the same parameter count,and outperforms other compared algorithms.In addition,on the GC10-DET dataset,the detection accuracy improved by 4.0%compared to the baseline model,indicating that the model has good generalization ability.

关 键 词：目标检测 缺陷检测 YOLOv8 可变形卷积 Inner-IoU 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]